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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsMicroservices patterns help solve specific problems in systems built from independently deployable, loosely coupled services—but they do not make the system simple by themselves. Start with business capabilities and clear ownership boundaries, then choose patterns for the communication, data, resilience, deployment, and testing problems your system actually has. A monolith or a simpler architecture may be the better choice when the benefits of independent services do not justify their operational cost.
What microservices patterns are—and what they cannot do
A microservices architecture divides an application into services that can be deployed independently and are loosely coupled. Patterns are reusable approaches to recurring design problems, such as finding a service instance, coordinating a workflow across independent data stores, or limiting the effects of a failing dependency.
Patterns are not a checklist. Adding a gateway, message broker, service registry, and orchestration platform does not automatically produce a sound architecture. Each adds behavior and operational responsibility. Microsoft describes the system-level complexity that comes with service discovery, consistency, transactions, and interservice communication; AWS recommends evaluating architecture choices against the specific application and its costs.
The AWS whitepaper Implementing Microservices on AWS puts the choice this way: “Deciding between microservices or monoliths should be made on a case-by-case basis, considering factors like scale, complexity, and specific use cases.”
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Choose boundaries before choosing infrastructure
Decompose around business capabilities
Business capabilities and domain subdomains are useful starting points for deciding where service boundaries belong. A boundary should group responsibilities that change together while limiting how much one service needs to know about another. The pattern catalog also describes self-contained services and service-per-team; these can be useful arrangements, but neither is a universal rule.
Make ownership explicit
Clear responsibility for each service’s domain and data makes it easier to evolve that service without coordinating every change across the system. Microsoft notes that a service owning its data and schema can reduce cross-service dependencies and support independent evolution. If several services routinely need direct access to the same tables or must be changed together, reconsider the boundary or the way they exchange information.
Use the Strangler Fig pattern for gradual replacement
For legacy modernization, the Strangler Fig pattern replaces selected functionality incrementally while consumers continue to use the existing interface during the transition. It is a migration strategy, not a one-step rewrite: establish a controlled boundary that directs the relevant behavior to either the old implementation or its replacement, then move functionality in manageable pieces. The interface and routing boundary need to remain clear while both implementations coexist.
Choose a monolith or microservices based on the problem
A monolith keeps functionality in one deployable application; microservices divide it into independently deployable units. Neither arrangement is inherently superior. Compare the real operating conditions and organizational needs of the system before creating service boundaries.
| Decision factor | A monolith may fit when | Microservices may fit when |
|---|---|---|
| Deployment | Coordinated releases are acceptable and independent deployment is not a strong requirement. | Teams need to release specific capabilities independently. |
| Operational complexity | The team benefits from one application and a simpler runtime and deployment model. | The team can operate service discovery, interservice communication, distributed data flows, and system-wide monitoring. |
| Team ownership | A single team or closely coordinated teams can own the application as a whole. | Service responsibilities map to teams that can own and evolve their areas with limited cross-team coordination. |
| Scale and use case | The application’s scale and requirements do not justify splitting it into independently operated services. | Specific needs make independently deployed services valuable enough to justify their cost. |
This is a qualitative decision, not a universal threshold. AWS advises weighing scale, complexity, use cases, and costs case by case. A modular monolith can also preserve internal boundaries while avoiding the operational commitments of remote services.
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Give clients a deliberate API boundary
API gateway
An API gateway offers clients a unified endpoint and can route requests, aggregate multiple backend requests, and centralize concerns such as authentication, SSL termination, and rate limiting. Concentrating responsibilities at the edge can simplify client access, but it also creates a component whose configuration and availability matter to the request path. Be explicit about which concerns belong there and which remain with individual services.
Backend for Frontend
A Backend for Frontend (BFF) creates a client-specific backend for a particular interface, such as mobile or desktop. It is useful when different clients have genuinely different data or interaction needs. A gateway is oriented toward a shared client-facing entry point; a BFF is oriented toward tailoring backend behavior to a client. A system can use both, but every additional layer increases operational complexity.
| Question | API gateway | Backend for Frontend |
|---|---|---|
| Primary job | Provide a unified entry point and route or aggregate requests. | Serve the distinct needs of a specific client type. |
| Useful when | Clients need a common edge for routing or shared gateway concerns. | Different clients need different response shapes or backend interactions. |
| Trade-off | Centralizes responsibilities and adds a critical component to the request path. | Can duplicate behavior or create more backends to maintain if client needs do not justify them. |
Choose service communication by interaction needs
Synchronous request-response
Remote procedure invocation suits interactions where a caller needs a response to continue. It is direct, but caller and callee are temporally coupled: a slow or unavailable callee affects the caller’s request. Set timeouts and define failure behavior rather than allowing a request to wait indefinitely.
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With asynchronous messaging, a sender places a message for a consumer, often through a broker. AWS notes that the consumer does not necessarily need to be online at the moment a message is sent. This can reduce temporal coupling, but shifts work into message handling and operations.
Choose between the approaches by asking whether the caller needs an immediate answer, what latency is acceptable, and how the system will handle delivery semantics, duplicate messages, ordering, and failures. Do not assume that a message is delivered exactly once or in order: those properties depend on the broker, configuration, and application design. Consumers that may see a message more than once should be designed with idempotency in mind.
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Keep data ownership and consistency separate
Database per service
With database per service, each service controls its own storage and data management. This supports autonomy and lets services evolve their data choices independently, but other services should not treat that store as their own. A workflow that crosses service-owned stores needs an explicit consistency strategy.
Sagas for cross-service workflows
A saga coordinates a workflow as a sequence of local transactions. If a later step fails, compensating transactions can counteract earlier steps. This is an alternative to relying on a distributed transaction across services, which Microsoft describes as often impractical in microservices. A compensation is application behavior, not a guaranteed reversal of history: define what each step does, what can be compensated, and how failures are handled.
Related data patterns solve different problems
- API Composition: combines query results owned by multiple services. It is a way to assemble a read response, not a replacement for service-owned data.
- CQRS: separates read and write models. It can address different needs on the read and write sides, but adds models and synchronization considerations.
- Domain events: communicate that something meaningful happened in a domain, allowing interested parts of the system to react.
- Event sourcing: represents state through a sequence of events. It is a distinct choice about how state is stored, not another name for messaging or domain events.
- Transactional outbox: addresses the problem of publishing a message atomically with a database transaction by recording the message for later publication as part of the transaction. It does not by itself decide how consumers handle duplicates or ordering.
These patterns can be combined, but their implementation details depend on the system. Select them to meet a defined consistency or query need rather than adopting them as a bundle.
Use service discovery when locations can change
Service discovery helps a caller or router find an instance when service locations change. A registry stores service-instance locations. In client-side discovery, the client consults the registry and chooses an instance; in server-side discovery, a routing component performs that lookup on the client’s behalf.
| Approach | Where lookup and routing happen | Decision to make |
|---|---|---|
| Client-side discovery | The caller consults the registry and selects an instance. | Whether clients should carry discovery and selection responsibility. |
| Server-side discovery | A router or other server-side component consults the registry and forwards the request. | Whether routing should be managed centrally rather than by each client. |
The pattern catalog describes both approaches and the registry concept. The right choice depends on where your system wants routing responsibility to live; discovery does not replace a policy for handling unavailable instances.
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Limit cascading failures with timeouts and circuit breakers
A circuit breaker sits between a caller and callee. It tracks failures, stops routing calls after a threshold is exceeded, and periodically checks whether the callee has recovered. When open, it returns an immediate failure rather than continuing to send requests to an unavailable service.
A circuit breaker is not a retry policy. Retrying a failing call without limits can add load to an already unhealthy dependency. Pair retries with timeouts and a clear failure policy, and consider whether an operation is safe to retry. AWS guidance also highlights timeout behavior, administrative control, multithreaded call considerations, and logging as design concerns. Set thresholds and recovery behavior for the application rather than assuming one configuration fits every dependency.
Select a deployment model for the operating environment
The pattern catalog includes multiple service instances per host, a host or container per service instance, and serverless deployment. These choices trade off isolation, density, and operational responsibility; none is the default for every workload.
| Deployment option | What to weigh |
|---|---|
| Multiple service instances per host | Density and shared-host operation versus the isolation needs of each service. |
| Host or container per service instance | Isolation and instance-level deployment choices versus the resources and platform work they require. |
| Serverless deployment | Workload fit and platform-managed operations versus the capabilities and constraints of the chosen platform. |
Container orchestration can manage scheduling, deployment, failure recovery, and autoscaling; Kubernetes is one example. Microsoft describes these capabilities but does not make orchestration a requirement for every microservices system. Choose based on isolation, density, operating burden, platform capabilities, and workload needs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make observability and testing part of the design
Trace requests across boundaries
A user request may cross several services, so a symptom in one component can originate elsewhere. Distributed tracing follows requests across service boundaries and can help locate bottlenecks. Centralized logs, metrics, application performance monitoring, exception tracking, and health checks provide complementary views of system behavior. Microsoft names OpenTelemetry as an example framework for visibility into application health and performance.
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Test interactions at more than one level
Service-component tests exercise a service component; consumer-driven contract tests check agreements between a consumer and provider. The pattern catalog describes both. They complement, rather than eliminate, end-to-end testing. Microsoft cautions that testing service dependencies and refactoring across service boundaries can be challenging, so keep contracts and ownership clear enough that changes can be checked without relying only on broad end-to-end tests.
A practical sequence for applying the patterns
- Start with the system need. Identify the deployment, ownership, scale, or change constraint that a service boundary is meant to address. If there is no concrete need for independent services, do not add them by default.
- Map business capabilities and data ownership. Draw candidate boundaries around domain responsibilities and identify which service owns each piece of data.
- Choose interaction styles per workflow. Use request-response when a caller needs an immediate answer; consider messaging when asynchronous processing or reduced temporal coupling serves the workflow.
- Specify consistency and failure behavior. For cross-service workflows, decide whether local transactions coordinated as a saga fit, and define compensations, timeouts, retry limits, and duplicate handling.
- Choose the client edge and discovery approach. Decide whether a gateway, a client-specific BFF, or both are justified, then place registry lookup and routing responsibility deliberately.
- Plan for operation and verification. Select a deployment model the team can operate, instrument cross-service requests, and include component and contract tests alongside any end-to-end coverage.
- Introduce complexity incrementally. When replacing a legacy system, use a controlled Strangler Fig boundary to move selected functionality instead of attempting an all-at-once rewrite.
Screenshot capture as an optional supporting service
Screenshot capture is not a core microservices design pattern. If a system needs to capture rendered web pages as part of a separate workflow, ScreenshotNeo offers a website screenshot API and MCP server. A caller can make a GET request with a URL and receive a PNG, JPEG, WebP, or PDF; see the ScreenshotNeo site and API documentation.
For example, a service can request a WebP capture with cURL:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpScreenshotNeo says it accepts cookie or consent banners like a visitor and removes 60+ known consent platforms, newsletter popups, and chat widgets before capture; each of those steps can be turned off. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed, and responses identify page verdict and billing status with X-Page-Verdict and X-Billed headers. It also provides an MCP server for AI agents, with the tools take_screenshot, get_page_info, and capture_pdf. The Free plan includes 1,000 screenshots per month with no card; paid plans start at $5 for 3,000 screenshots. Every feature is available on every plan.
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Quick Recap
Common design mistakes to avoid
- Splitting services by technical layer alone: boundaries organized around business capabilities or domain subdomains are a stronger starting point than arbitrary component boundaries.
- Sharing databases as though ownership were shared: database per service only supports autonomy when services respect the owner’s control of its data and schema.
- Treating messaging as automatic reliability: broker-based communication changes availability coupling, but delivery, ordering, duplicate handling, and latency still require deliberate design.
- Adding retries without an outage policy: unbounded or poorly coordinated retries can intensify load on a failing dependency; combine retry decisions with timeouts and circuit-breaker behavior where appropriate.
- Choosing infrastructure before operating needs: orchestration, gateways, registries, and per-service deployment models all create responsibilities that should be justified by the workload and team.
- Depending only on end-to-end tests: service-component and consumer-driven contract tests address interaction risks closer to the service boundary, while distributed tracing helps diagnose runtime behavior.
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